H. E. Aguirre and K. Tanaka, Working principles, behavior, and performance of MOEAs on MNK-landscapes, European Journal of Operational Research, vol.181, issue.3, pp.1670-1690, 2007.
DOI : 10.1016/j.ejor.2006.08.004

H. Akaike, Information theory as an extension of the maximum likelihood principle, Second International Symposium on Information Theory, pp.267-281, 1973.

K. A. Andersen, K. J-¨-ornsten, L. , and M. , On bicriterion minimal spanning trees: An approximation, Computers & Operations Research, vol.23, issue.12, pp.1171-1182, 1996.
DOI : 10.1016/S0305-0548(96)00026-3

A. Auger and N. Hansen, Performance Evaluation of an Advanced Local Search Evolutionary Algorithm, 2005 IEEE Congress on Evolutionary Computation, pp.1777-1784, 2005.
DOI : 10.1109/CEC.2005.1554903

D. J. Barr, R. Levy, C. Scheepers, and H. J. Tily, Random effects structure for confirmatory hypothesis testing: Keep it maximal, Journal of Memory and Language, vol.68, issue.3, pp.255-278, 2013.
DOI : 10.1016/j.jml.2012.11.001

K. Bartó-n, MuMIn: Multi-Model Inference, 2014.

D. Bates, M. Mächler, B. Bolker, and S. Walker, Fitting linear mixed-effects models using lme4. arXiv preprint, 2014.

B. Bischl, O. Mersmann, H. Trautmann, and M. Preuß, Algorithm selection based on exploratory landscape analysis and cost-sensitive learning, Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference, GECCO '12, pp.313-320, 2012.
DOI : 10.1145/2330163.2330209

P. Borges and M. Hansen, A basis for future successes in multiobjective combinatorial optimization, 1998.

P. Breheny and W. Burchett, visreg: Visualization of Regression Models, 2015.

K. P. Burnham and D. R. Anderson, Model selection and multimodel inference: a practical information-theoretic approach, 2002.
DOI : 10.1007/b97636

K. P. Burnham and D. R. Anderson, Multimodel inference understanding AIC and BIC in model selection. Sociological methods & research, pp.261-304, 2004.

M. Chiarandini and Y. Goegebeur, Mixed Models for the Analysis of Optimization Algorithms, Experimental Methods for the Analysis of Optimization Algorithms, pp.225-264, 2010.
DOI : 10.1007/978-3-642-02538-9_10

C. A. Coello and G. B. Lamont, Applications of Multi-Objective Evolutionary Algorithms Advances in Natural Computation, 2004.

C. A. Coello, G. B. Lamont, V. Veldhuizen, and D. A. , Evolutionary Algorithms for Solving Multi-Objective Problems, 2007.
DOI : 10.1007/978-1-4757-5184-0

F. Daolio, A. Liefooghe, S. Verel, H. Aguirre, and K. Tanaka, Global vs Local Search on Multi-objective NK-Landscapes, Proceedings of the 2015 on Genetic and Evolutionary Computation Conference, GECCO '15, pp.369-376, 2015.
DOI : 10.1145/2739480.2754745
URL : https://hal.archives-ouvertes.fr/hal-01151882

F. Daolio, S. Verel, G. Ochoa, and M. Tomassini, Local optima networks and the performance of iterated local search, Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference, GECCO '12, pp.369-376, 2012.
DOI : 10.1145/2330163.2330217
URL : https://hal.archives-ouvertes.fr/hal-00741725

C. Davis, J. Hyde, S. Bangdiwala, N. , and J. , An example of dependencies among variables in a conditional logistic regression. Modern statistical methods in chronic disease epidemiology, pp.140-147, 1986.

K. Deb, Multi-Objective Optimization using Evolutionary Algorithms, 2001.

M. Ehrgott and K. Klamroth, Connectedness of efficient solutions in multiple criteria combinatorial optimization, European Journal of Operational Research, vol.97, issue.1, pp.159-166, 1997.
DOI : 10.1016/S0377-2217(96)00116-6

J. J. Faraway, Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models. Texts in Statistical Science, 2005.

J. Fox and G. Monette, Generalized Collinearity Diagnostics, Journal of the American Statistical Association, vol.10, issue.417, pp.178-183, 1992.
DOI : 10.1002/9780470316436

D. Garrett and D. Dasgupta, Multiobjective Landscape Analysis and the Generalized Assignment Problem, Learning and Intelligent OptimizatioN, pp.110-124, 2007.
DOI : 10.1016/S0305-0548(96)00032-9

D. Garrett and D. Dasgupta, Plateau Connection Structure and multiobjective metaheuristic performance, 2009 IEEE Congress on Evolutionary Computation, pp.1281-1288, 2009.
DOI : 10.1109/CEC.2009.4983092

J. Gorski, K. Klamroth, R. , and S. , Connectedness of Efficient Solutions in Multiple Objective Combinatorial Optimization, Journal of Optimization Theory and Applications, vol.36, issue.1, pp.475-497, 2011.
DOI : 10.1007/s10957-011-9849-8

F. E. Harrell-jr, rms: Regression Modeling Strategies. R package version 4, pp.5-5, 2016.

R. B. Heckendorn and D. Whitley, A walsh analysis of NK-landscapes, International Conference on Genetic Algorithms, pp.41-48, 1997.

F. Hutter, L. Xu, H. H. Hoos, and K. Leyton-brown, Algorithm runtime prediction: Methods & evaluation, Artificial Intelligence, vol.206, pp.79-111, 2014.
DOI : 10.1016/j.artint.2013.10.003

S. A. Kauffman, The Origins of Order, 1993.

J. Knowles and D. Corne, Instance Generators and Test Suites for the Multiobjective Quadratic Assignment Problem, Evolutionary Multi-Criterion Optimization, pp.295-310, 2003.
DOI : 10.1007/3-540-36970-8_21

W. A. Larsen and S. J. Mccleary, The Use of Partial Residual Plots in Regression Analysis, Technometrics, vol.33, issue.3, pp.781-790, 1972.
DOI : 10.1080/00401706.1972.10488966

M. Laumanns, L. Thiele, and E. Zitzler, Running time analysis of evolutionary algorithms on a simplified multiobjective knapsack problem, Natural Computing, vol.3, issue.1, pp.37-51, 2004.
DOI : 10.1023/B:NACO.0000023415.22052.55

M. Laumanns, L. Thiele, E. Zitzler, E. Welzl, D. et al., Running Time Analysis of Multi-objective Evolutionary Algorithms on a Simple Discrete Optimization Problem, Conference on Parallel Problem Solving From Nature (PPSN VII), volume 2439 of Lecture Notes in Computer Science, pp.44-53, 2002.
DOI : 10.1007/3-540-45712-7_5

A. Liefooghe, L. Jourdan, and E. Talbi, A software framework based on a conceptual unified model for evolutionary multiobjective optimization: ParadisEO-MOEO, European Journal of Operational Research, vol.209, issue.2, pp.104-112, 2011.
DOI : 10.1016/j.ejor.2010.07.023
URL : https://hal.archives-ouvertes.fr/hal-00522612

A. Liefooghe, L. Paquete, and J. R. Figueira, On Local Search for Bi-objective Knapsack Problems, Evolutionary Computation, vol.21, issue.1, pp.179-196, 2013.
DOI : 10.1109/TEVC.2003.810758
URL : https://hal.archives-ouvertes.fr/hal-00676625

A. Liefooghe, S. Verel, H. Aguirre, and K. Tanaka, What Makes an Instance Difficult for Black-Box 0???1 Evolutionary Multiobjective Optimizers?, International Conference on Artificial Evolution, pp.3-15, 2013.
DOI : 10.1007/978-3-319-11683-9_1
URL : https://hal.archives-ouvertes.fr/hal-00855252

A. Mcleod, Kendall: Kendall rank correlation and Mann-Kendall trend test, 2011.

O. Mersmann, B. Bischl, J. Bossek, H. Trautmann, M. Wagner et al., Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness, Learning and Intelligent OptimizatioN Conference, pp.115-129, 2012.
DOI : 10.1007/978-3-642-34413-8_9

O. Mersmann, B. Bischl, H. Trautmann, M. Preuss, C. Weihs et al., Exploratory landscape analysis, Proceedings of the 13th annual conference on Genetic and evolutionary computation, GECCO '11, pp.829-836, 2011.
DOI : 10.1145/2001576.2001690

P. Merz, Advanced Fitness Landscape Analysis and the Performance of Memetic Algorithms, Evolutionary Computation, vol.13, issue.4, pp.303-325, 2004.
DOI : 10.1109/4235.887236

J. Mote, I. Murthy, and D. L. Olson, A parametric approach to solving bicriterion shortest path problems, European Journal of Operational Research, vol.53, issue.1, pp.81-92, 1991.
DOI : 10.1016/0377-2217(91)90094-C

S. Nakagawa and H. Schielzeth, from generalized linear mixed-effects models, Methods in Ecology and Evolution, vol.22, issue.2, pp.133-142, 2013.
DOI : 10.1111/j.2041-210x.2012.00261.x

O. 'brien and R. M. , A caution regarding rules of thumb for variance inflation factors, Quality & Quantity, vol.41, issue.5, pp.673-690, 2007.

C. H. Papadimitriou and M. Yannakakis, On the approximability of trade-offs and optimal access of Web sources, Proceedings 41st Annual Symposium on Foundations of Computer Science, pp.86-92, 2000.
DOI : 10.1109/SFCS.2000.892068

L. Paquete, C. Camacho, and J. R. Figueira, A two-phase heuristic for the biobjective 0/1 knapsack problem, 2008.

L. Paquete, M. Chiarandini, and T. Stützlest¨stützle, Pareto Local Optimum Sets in the Biobjective Traveling Salesman Problem: An Experimental Study, Metaheuristics for Multiobjective Optimisation, pp.177-199, 2004.
DOI : 10.1007/978-3-642-17144-4_7

L. Paquete, T. Schiavinotto, and T. Stützlest¨stützle, On local optima in multiobjective combinatorial optimization problems, Annals of Operations Research, vol.7, issue.2, pp.83-97, 2007.
DOI : 10.1007/s10479-007-0230-0

L. Paquete and T. Stützlest¨stützle, A study of stochastic local search algorithms for the biobjective QAP with correlated flow matrices, European Journal of Operational Research, vol.169, issue.3, pp.943-959, 2006.
DOI : 10.1016/j.ejor.2004.08.024

L. Paquete and T. Stützlest¨stützle, Clusters of Non-dominated Solutions in Multiobjective Combinatorial Optimization: An Experimental Analysis, Multiobjective Programming and Goal Programming, pp.69-77, 2009.
DOI : 10.1007/978-3-540-85646-7_7

R. Team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, 2015.

H. Richter and A. Engelbrecht, Recent Advances in the Theory and Application of Fitness Landscapes. Emergence, Complexity and Computation, 2014.

S. Verel, A. Liefooghe, L. Jourdan, and C. Dhaenens, Analyzing the Effect of Objective Correlation on the Efficient Set of MNK-Landscapes, Learning and Intelligent OptimizatioN, pp.238-252, 2011.
DOI : 10.1007/BF00202749
URL : https://hal.archives-ouvertes.fr/hal-00550349

S. Verel, A. Liefooghe, L. Jourdan, and C. Dhaenens, On the structure of multiobjective combinatorial search space: MNK-landscapes with correlated objectives, European Journal of Operational Research, vol.227, issue.2, pp.331-342, 2013.
DOI : 10.1016/j.ejor.2012.12.019
URL : https://hal.archives-ouvertes.fr/hal-00760097

T. Wagner, N. Beume, and B. Naujoks, Pareto-, Aggregation-, and Indicator-Based Methods in Many-Objective Optimization, Evolutionary Multi-Criterion Optimization, pp.742-756, 2007.
DOI : 10.1007/978-3-540-70928-2_56

E. D. Weinberger, Correlated and uncorrelated fitness landscapes and how to tell the difference, Biological Cybernetics, vol.1, issue.5, pp.325-336, 1990.
DOI : 10.1007/BF00202749

E. D. Weinberger, Local properties of Kauffman's N-k model: A tunably rugged energy landscape, Physical Review A, issue.10, p.446399, 1991.

H. Wickham, ggplot2: elegant graphics for data analysis, 2009.

E. Zitzler, L. Thiele, M. Laumanns, C. M. Foneseca, G. Da-fonseca et al., Performance assessment of multiobjective optimizers: an analysis and review, IEEE Transactions on Evolutionary Computation, vol.7, issue.2, pp.117-132, 2003.
DOI : 10.1109/TEVC.2003.810758